Inference from Sampling with Response Probabilities Estimated via Calibration
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A solution to control for nonresponse bias consists of multiplying the design weights of respondents by the inverse of estimated response probabilities to compensate for the nonrespondents. Maximum likelihood and calibration are two approaches that can be applied to obtain estimated response probabilities. The paper develops asymptotic properties of the resulting estimator when calibration is applied. A logistic regression model for the response probabilities is postulated and missing at random data is supposed. The author shows that the estimators with the response probabilities estimated via calibration are asymptotically equivalent to unbiased estimators and that a gain in efficiency is obtained when estimating the response probabilities via calibration as compared to the estimator with the true response probabilities.
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